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Found 4 papers, 1 papers with code
Low-cost Real-world Implementation of the Swing-up Pendulum for Deep Reinforcement Learning Experiments
Deep reinforcement learning (DRL) has had success in virtual and simulated domains, but due to key differences between simulated and real-world environments, DRL-trained policies have had limited success in real-world applications.
Training Directional Locomotion for Quadrupedal Low-Cost Robotic Systems via Deep Reinforcement Learning
In particular, we exploit randomization of heading that the robot must follow to foster exploration of action-state transitions most useful for learning both forward locomotion as well as course adjustments.
Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition tasks as opposed to a common frame-to-frame retrieval problem formulation currently implemented in SOTA place recognition methods.
Towards Multidimensional Textural Perception and Classification Through Whisker
Further, we experimentally validate that the sensor can classify texture with roughness depths as low as $2. 5\mu m$ at an accuracy of $90\%$ or more and segregate materials based on their roughness and hardness.
